Maya Kasowski
Impact in
- Genetics top 2%
- Genetic Associations and Epidemiology
- Cancer Research top 5%
- Cancer-related molecular mechanisms research
Papers in
-
- Genomics and Chromatin Dynamics 9
- RNA Research and Splicing 4
- RNA and protein synthesis mechanisms 2
- Metabolomics and Mass Spectrometry Studies 2
- RNA modifications and cancer 1
- PI3K/AKT/mTOR signaling in cancer 1
- Gene expression and cancer classification 1
- Genetics 3
- Genetic Associations and Epidemiology 2
- Co-authors
- M Snyder (8 shared papers)Manoj Hariharan (3 shared papers)Konrad J. Karczewski (2 shared papers)Alan P. Boyle (2 shared papers)Eurie L. Hong (1 shared paper)Shuai Weng (1 shared paper)Marc A. Schaub (1 shared paper)Benjamin C. Hitz (1 shared paper)
- Journals
- Nature Communications (2 papers)Science (2 papers)Genome Research (2 papers)Human Genomics (1 paper)Brain (1 paper)
- Partner nations
- United StatesGermanySpain
In The Last Decade
Maya Kasowski
13 papers receiving 3.1k citations
Maya Kasowski's Hit Papers
Peers
Comparison fields: 5 of 108
- Genetics 996
- Cancer Research 375
- Molecular Biology 1.8k
- Immunology 227
- Aging 19
Countries citing papers authored by Maya Kasowski
This map shows the geographic impact of Maya Kasowski's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Maya Kasowski with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maya Kasowski more than expected).
Fields of papers citing papers by Maya Kasowski
This network shows the impact of papers produced by Maya Kasowski. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Maya Kasowski. The network helps show where Maya Kasowski may publish in the future.
Co-authors
The 25 scholars most cited alongside Maya Kasowski, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Annotation of functional variation in personal genomes using RegulomeDB Hit paper breakdown → | 2012 | 1735 |
| 2 | 2010 | 428 | |
| 3 | 2013 | 247 | |
| 4 | 2015 | 215 | |
| 5 | 2014 | 208 | |
| 6 | 2020 | 119 | |
| 7 | 2020 | 50 | |
| 8 | 2020 | 33 | |
| 9 | 2021 | 23 | |
| 10 | 2010 | 13 | |
| 11 | 2025 | 4 | |
| 12 | 2025 | 1 | |
| 13 | 2024 | 1 | |
| 14 | 2025 | 0 | |
| 15 | 2025 | 0 | |
| 16 | 2024 | 0 |
About Maya Kasowski
Maya Kasowski is a scholar working on Molecular Biology, Genetics, Pulmonary and Respiratory Medicine, Oncology and Clinical Biochemistry, having authored 16 papers that have together received 3.1k indexed citations. Recurring topics across this work include Genomics and Chromatin Dynamics (9 papers), RNA Research and Splicing (4 papers), RNA and protein synthesis mechanisms (2 papers), Metabolomics and Mass Spectrometry Studies (2 papers), Genetic Associations and Epidemiology (2 papers), RNA modifications and cancer (1 paper), PI3K/AKT/mTOR signaling in cancer (1 paper) and Gene expression and cancer classification (1 paper). The work is most often cited by research in Genetics (996 citations), Cancer Research (375 citations), Molecular Biology (1.8k citations), Immunology (227 citations) and Aging (19 citations). Maya Kasowski has collaborated with scholars based in United States, Germany and Spain. Frequent co-authors include M Snyder, Manoj Hariharan, Konrad J. Karczewski, Alan P. Boyle, Eurie L. Hong, Shuai Weng, Marc A. Schaub, Benjamin C. Hitz, Julie Park and J. Michael Cherry. Their work appears in journals such as Nature Communications, Science, Genome Research, Human Genomics and Brain.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.